2023-03-01

Background

The Equity Center

Mission: Tangibly redress racial and economic inequity in university communities by advancing a transformative approach to the fundamental research mission.

Vision: Universities that serve local communities by bringing rich research resources to bear on the work of redressing poverty and racial inequality and equip students to lead in building a just society.

Democratization of Data

The Democratization of Data Initiative centers community-driven partnership to provide advocates as well as civic-and private-sector leaders with data and metrics, contextualized analysis, interactive maps and data visualizations, and narrative storytelling as a resource in pursuit of equity throughout the region.

Public Interest Data: Ethics & Practice

a class in

Course goals

  • Make progress on projects that advance social justice and policy understanding in collaboration with community partners.
  • Practice working with data to answer pressing questions, including finding, cleaning, and understanding data; exploring, analyzing, modeling data; visualizing, contextualizing, and communicating data; with care and humility and respect for the affected partners and communities throughout.
  • Develop experience in data workflows that support ethical data science, including processes for working collaboratively, openly, inclusively, and reproducibly.

Stepping Stones Report

Steppings Stones: A Report on Community Well-Being of Children and Families in the Charlottesville/Albemarle Area

  • Produced by the City of Charlottesville Department of Human Services
  • A compilation of ~ 40 measures to track community and youth well-being
  • Began as a project of the Commission for Children and Families in 2000

Principles

How do we enact equity and ethics in our data work?

Open Knowledge

  • Open (authoritative) sources when possible
    • Challenges of making ad hoc data requests; mediated sources no longer available
    • Generalizability to other places
    • Example: VDSS reports on foster care
    • Example: VSS CPS reports
  • Open software ; with learning curve
  • Open access, ideally online (in multiple places) and in printed form

Reproducibility and Reuse

Shared on GitHub

  • Generate processes that are repeatable, preferably by a computer (via scripting); examples VDSS, VDH
  • Generate proceses that are understandable, preferably by a human (via commenting), examples VDOE, SAIPE

https://xkcd.com/242/

Transparency and Documentation

Facilitate validation

  • Re-collection of past data when possible; example BOE
  • Redundancy for data that requires manual curation; example DCV reports
  • Documentation of choices for checking by others

https://xkcd.com/1421/

Data don’t speak for themselves

Who’s view is communicated in the presentation of the data? (an aside)

Contextualization

Some categories we’re working to address for each measure…

  • Why is this important?
  • How is this measured?
  • Source of the data
  • Description of trend
  • Further considerations (data settings)
  • Who is this data about?

Next steps

  • Students submit their final drafts tomorrow!
  • Equity Center team will check, integrate, complete
  • A smaller student team will begin work of generating a selection of measures disaggregated by race
  • DHS will work with us to convene a set of stakeholders for pre-release review and feedback

Thank you!